This paper focuses on improving customer service in call centers, where finding accurate answers in the shortest possible time is crucial. The proposed solution is the development of a conversational AI system that acts as a "copilot" for human operators. The main goal of this copilot is to assist the operator in real time by providing conversation summaries, relevant domain information, and suggested responses that help guide the interaction toward a successful resolution. To achieve this, different approaches to Retrieval Augmented Generation (RAG) have been explored. The proposed agentic-RAG architecture integrates multiple autonomous agents for routing, retrieval validation, and response generation, achieving consistent improvements in real-time performance, grounding, and overall user experience across diverse service scenarios. Empirical results with the Action-Based Conversations Dataset (ABCD) corpus show that the use of agents proved to be effective in handling unstructured conversational data. The proposed approach showed an improvement in the quality, relevance, and accuracy of the generated responses with respect to a naïve RAG baseline. It is important to emphasize that this system is not intended to replace the operator, but rather to act as a support tool to enhance efficiency and customer satisfaction.